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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,492 @@
----
-license: mit
----
+---
+base_model: Mihaiii/Venusaur
+datasets:
+- Mihaiii/qa-assistant-2
+language:
+- en
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:16011
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: What impact does high-speed rail have on connectivity between cities?
+  sentences:
+  - Art supplies can be quite expensive, especially high-quality paints and brushes.
+  - High-speed rail can be a more comfortable and convenient mode of travel compared
+    to buses or cars.
+  - Engineers use a variety of methods to test the safety of autonomous vehicles,
+    including controlled track testing and public road trials.
+- source_sentence: What is the best soil type for growing tomatoes?
+  sentences:
+  - Sandy loam soil is often considered ideal for growing tomatoes due to its good
+    drainage and nutrient-holding capacity.
+  - Socialist political systems are often contrasted with capitalist systems, which
+    prioritize private ownership and market-driven economies.
+  - The core principles of Sikhism include the belief in one God, the importance of
+    honest living, and the practice of selfless service.
+- source_sentence: What are the three main types of rocks?
+  sentences:
+  - Mount Everest is the highest mountain in the world, located in the Himalayas.
+  - Archaeologists sometimes face challenges such as funding and access to advanced
+    technology, which can impact their ability to preserve findings.
+  - Some people are concerned about the ethical implications of genetic modification
+    in food production.
+- source_sentence: How do vaccines help prevent diseases?
+  sentences:
+  - The theory also posits that during periods of economic downturn, increased government
+    spending can help stimulate demand and pull the economy out of recession.
+  - The Gurdwara is a place where Sikhs can participate in religious rituals and ceremonies,
+    such as weddings and naming ceremonies.
+  - The development of vaccines involves rigorous testing to ensure their safety and
+    efficacy before they are approved for public use.
+- source_sentence: What are the social structures of ants?
+  sentences:
+  - The social hierarchy of ants is a complex system that ensures the survival and
+    efficiency of the colony.
+  - In a parliamentary system, the executive branch derives its legitimacy from and
+    is accountable to the legislature; the executive and legislative branches are
+    thus interconnected.
+  - Proper waste management and recycling can contribute to a more sustainable farming
+    operation.
+model-index:
+- name: SentenceTransformer based on Mihaiii/Venusaur
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.826101669872389
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8277251878978443
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8199515763304537
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8225731321378551
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8214525375708358
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8236879484111633
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8037304918463798
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8082305683494836
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.826101669872389
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8277251878978443
+      name: Spearman Max
+---
+
+# SentenceTransformer based on Mihaiii/Venusaur
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) on the [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) <!-- at revision 0dc817f0addbb7bab8feeeeaded538f9ffeb3419 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What are the social structures of ants?',
+    'The social hierarchy of ants is a complex system that ensures the survival and efficiency of the colony.',
+    'In a parliamentary system, the executive branch derives its legitimacy from and is accountable to the legislature; the executive and legislative branches are thus interconnected.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8261     |
+| **spearman_cosine** | **0.8277** |
+| pearson_manhattan   | 0.82       |
+| spearman_manhattan  | 0.8226     |
+| pearson_euclidean   | 0.8215     |
+| spearman_euclidean  | 0.8237     |
+| pearson_dot         | 0.8037     |
+| spearman_dot        | 0.8082     |
+| pearson_max         | 0.8261     |
+| spearman_max        | 0.8277     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Mihaiii/qa-assistant-2
+
+* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241)
+* Size: 16,011 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                             | score                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | float                                                           |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 12.73 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.42 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
+* Samples:
+  | question                                                               | answer                                                                                                                                    | score                            |
+  |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>Can you describe the process of robot path planning?</code>      | <code>Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes.</code> | <code>0.27999999999999997</code> |
+  | <code>Can humans live on Mars?</code>                                  | <code>Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance.</code>                     | <code>0.16</code>                |
+  | <code>What are the key elements of composition in abstract art?</code> | <code>The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian.</code>    | <code>0.36</code>                |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Mihaiii/qa-assistant-2
+
+* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241)
+* Size: 3,879 evaluation samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                             | score                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | float                                                           |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 12.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.63 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
+* Samples:
+  | question                                                                                                           | answer                                                                                                                                                                           | score                            |
+  |:-------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>What is the concept of social stratification?</code>                                                         | <code>The study of social stratification involves examining the inequalities and divisions within a society.</code>                                                              | <code>0.6799999999999999</code>  |
+  | <code>How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series?</code> | <code>The 'Harry Potter' series consists of seven books, starting with 'Harry Potter and the Philosopher's Stone' and ending with 'Harry Potter and the Deathly Hallows'.</code> | <code>0.22000000000000003</code> |
+  | <code>What is the parliamentary system and how does it function?</code>                                            | <code>In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections.</code>                                             | <code>0.6799999999999999</code>  |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|
+| 0.0999 | 100  | 0.0593        | 0.0540 | 0.5848                  |
+| 0.1998 | 200  | 0.05          | 0.0463 | 0.6618                  |
+| 0.2997 | 300  | 0.044         | 0.0418 | 0.7102                  |
+| 0.3996 | 400  | 0.0413        | 0.0385 | 0.7390                  |
+| 0.4995 | 500  | 0.0377        | 0.0349 | 0.7707                  |
+| 0.5994 | 600  | 0.034         | 0.0333 | 0.7770                  |
+| 0.6993 | 700  | 0.0344        | 0.0321 | 0.7879                  |
+| 0.7992 | 800  | 0.0324        | 0.0311 | 0.7927                  |
+| 0.8991 | 900  | 0.0334        | 0.0302 | 0.8005                  |
+| 0.9990 | 1000 | 0.0304        | 0.0305 | 0.8023                  |
+| 1.0989 | 1100 | 0.0261        | 0.0306 | 0.8072                  |
+| 1.1988 | 1200 | 0.0267        | 0.0292 | 0.8104                  |
+| 1.2987 | 1300 | 0.0244        | 0.0287 | 0.8110                  |
+| 1.3986 | 1400 | 0.0272        | 0.0294 | 0.8098                  |
+| 1.4985 | 1500 | 0.0241        | 0.0281 | 0.8135                  |
+| 1.5984 | 1600 | 0.0253        | 0.0282 | 0.8143                  |
+| 1.6983 | 1700 | 0.0245        | 0.0276 | 0.8169                  |
+| 1.7982 | 1800 | 0.025         | 0.0274 | 0.8182                  |
+| 1.8981 | 1900 | 0.0236        | 0.0273 | 0.8193                  |
+| 1.9980 | 2000 | 0.0236        | 0.0269 | 0.8218                  |
+| 2.0979 | 2100 | 0.0215        | 0.0278 | 0.8213                  |
+| 2.1978 | 2200 | 0.0216        | 0.0269 | 0.8226                  |
+| 2.2977 | 2300 | 0.0205        | 0.0276 | 0.8207                  |
+| 2.3976 | 2400 | 0.0181        | 0.0273 | 0.8202                  |
+| 2.4975 | 2500 | 0.0197        | 0.0267 | 0.8228                  |
+| 2.5974 | 2600 | 0.02          | 0.0267 | 0.8238                  |
+| 2.6973 | 2700 | 0.0203        | 0.0263 | 0.8258                  |
+| 2.7972 | 2800 | 0.0184        | 0.0263 | 0.8264                  |
+| 2.8971 | 2900 | 0.0201        | 0.0269 | 0.8243                  |
+| 2.9970 | 3000 | 0.0196        | 0.0263 | 0.8251                  |
+| 3.0969 | 3100 | 0.0168        | 0.0264 | 0.8250                  |
+| 3.1968 | 3200 | 0.0176        | 0.0263 | 0.8267                  |
+| 3.2967 | 3300 | 0.0168        | 0.0263 | 0.8270                  |
+| 3.3966 | 3400 | 0.017         | 0.0260 | 0.8277                  |
+| 3.4965 | 3500 | 0.0164        | 0.0261 | 0.8273                  |
+| 3.5964 | 3600 | 0.0172        | 0.0259 | 0.8280                  |
+| 3.6963 | 3700 | 0.0168        | 0.0260 | 0.8274                  |
+| 3.7962 | 3800 | 0.0176        | 0.0262 | 0.8279                  |
+| 3.8961 | 3900 | 0.0182        | 0.0261 | 0.8278                  |
+| 3.9960 | 4000 | 0.0174        | 0.0260 | 0.8277                  |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.0.1+cu118
+- Accelerate: 0.31.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "Mihaiii/Venusaur",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.0.1+cu118"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b7fba5907abae73be1381971d33455a4d60264dedc2143d95301cd1733d75dc
+size 62465680

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,44 @@
+{
+  "additional_special_tokens": [
+    "[PAD]",
+    "[UNK]",
+    "[CLS]",
+    "[SEP]",
+    "[MASK]"
+  ],
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,71 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "[PAD]",
+    "[UNK]",
+    "[CLS]",
+    "[SEP]",
+    "[MASK]"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}